# LLM-Distilled Rephrasing Rules

This work characterizes the language sensitivity of vision-language-action models (VLAs): one-word edits can move success by tens of points, and phrasing alone nearly closes the 21-point gap between in-distribution and out-of-distribution tasks; the authors have a large language model distill scored phrasings of a few training tasks into ten to twenty rephrasing rules, rewrite each incoming instruction once at deployment, and thereby improve the frozen policy by 16–27% relative on twelve held-out tasks without modifying its weights, lifting in-finetune success on LIBERO from 93.6% to 97.8%.

This work characterizes the language sensitivity of vision-language-action models (VLAs): one-word edits can move success by tens of points, and phrasing alone nearly closes the 21-point gap between in-distribution and out-of-distribution tasks; the authors have a large language model distill scored phrasings of a few training tasks into ten to twenty rephrasing rules, rewrite each incoming instruction once at deployment, and thereby improve the frozen policy by 16–27% relative on twelve held-out tasks without modifying its weights, lifting in-finetune success on LIBERO from 93.6% to 97.8%.